Machine Learning Identification of Obstructive Sleep Apnea Severity through the Patient Clinical Features: A Retrospective Study
Antonino Maniaci, Paolo Marco Riela, Giannicola Iannella, Jérôme R. Lechien, Ignazio La Mantia, Marco de Vincentiis, Giovanni Cammaroto, Christian Calvo‐Henríquez, Milena Di Luca, Carlos M. Chiesa‐Estomba, Alberto Maria Saibene, Isabella Pollicina, Giovanna Stilo, Paola Di Mauro, Angelo Cannavicci, Rodolfo Lugo, Giuseppe Magliulo, Antonio Greco, Annalisa Pace, Giuseppe Meccariello, Salvatore Cocuzza, Claudio Vicini
Abstract
OBJECTIVES: To evaluate the role of clinical scores assessing the risk of disease severity in patients with clinical suspicion of obstructive sleep apnea syndrome (OSA). The hypothesis was tested by applying artificial intelligence (AI) to demonstrate its effectiveness in distinguishing between mild-moderate OSA and severe OSA risk. METHODS: A support vector machine model (SVM) was developed from the samples included in the analysis (N = 498), and they were split into 75% for training (N = 373) with the remaining for testing (N = 125). Two diagnostic thresholds were selected for OSA severity: mild to moderate (apnea-hypopnea index (AHI) ≥ 5 events/h and AHI < 30 events/h) and severe (AHI ≥ 30 events/h). The algorithms were trained and tested to predict OSA patient severity. RESULTS: The sensitivity and specificity for the SVM model were 0.93 and 0.80 with an accuracy of 0.86; instead, the logistic regression full mode reported a value of 0.74 and 0.63, respectively, with an accuracy of 0.68. After backward stepwise elimination for features selection, the reduced logistic regression model demonstrated a sensitivity and specificity of 0.79 and 0.56, respectively, and an accuracy of 0.67. CONCLUSION: Artificial intelligence could be applied to patients with symptoms related to OSA to identify individuals with a severe OSA risk with clinical-based algorithms in the OSA framework.